PDF(Pubmed)
Abstract：
UNASSIGNED: (1) To assess the methodological quality and risk of bias of radiomics studies investigating the diagnostic performance in adrenal masses and (2) to determine the potential diagnostic value of radiomics in adrenal tumors by quantitative analysis.
UNASSIGNED: PubMed, Embase, Web of Science, and Cochrane Library databases were searched for eligible literature. Methodological quality and risk of bias in the included studies were assessed by the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS). The diagnostic performance was evaluated by pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Spearman\'s correlation coefficient and subgroup analysis were used to investigate the cause of heterogeneity. Publication bias was examined using the Deeks\' funnel plot.
UNASSIGNED: Twenty-eight studies investigating the diagnostic performance of radiomics in adrenal tumors were identified, with a total of 3579 samples. The average RQS was 5.11 (14.2% of total) with an acceptable inter-rater agreement (ICC 0.94, 95% CI 0.93-0.95). The risk of bias was moderate according to the result of QUADAS-2. Nine studies investigating the use of CT-based radiomics in differentiating malignant from benign adrenal tumors were included in the quantitative analysis. The pooled sensitivity, specificity, DOR and AUC with 95% confidence intervals were 0.80 (0.68-0.88), 0.83 (0.73-0.90), 19.06 (7.87-46.19) and 0.88 (0.85-0.91), respectively. There was significant heterogeneity among the included studies but no threshold effect in the meta-analysis. The result of subgroup analysis demonstrated that radiomics based on unenhanced and contrast-enhanced CT possessed higher diagnostic performance, and second-order or higher-order features could enhance the diagnostic sensitivity but also increase the false positive rate. No significant difference in diagnostic ability was observed between studies with machine learning and those without.
UNASSIGNED: The methodological quality and risk of bias of studies investigating the diagnostic performance of radiomics in adrenal tumors should be further improved in the future. CT-based radiomics has the potential benefits in differentiating malignant from benign adrenal tumors. The heterogeneity between the included studies was a major limitation to obtaining more accurate conclusions.
UNASSIGNED: https://www.crd.york.ac.uk/PROSPERO/ CRD 42022331999 .
UNASSIGNED: PubMed, Embase, Web of Science, and Cochrane Library databases were searched for eligible literature. Methodological quality and risk of bias in the included studies were assessed by the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS). The diagnostic performance was evaluated by pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Spearman\'s correlation coefficient and subgroup analysis were used to investigate the cause of heterogeneity. Publication bias was examined using the Deeks\' funnel plot.
UNASSIGNED: Twenty-eight studies investigating the diagnostic performance of radiomics in adrenal tumors were identified, with a total of 3579 samples. The average RQS was 5.11 (14.2% of total) with an acceptable inter-rater agreement (ICC 0.94, 95% CI 0.93-0.95). The risk of bias was moderate according to the result of QUADAS-2. Nine studies investigating the use of CT-based radiomics in differentiating malignant from benign adrenal tumors were included in the quantitative analysis. The pooled sensitivity, specificity, DOR and AUC with 95% confidence intervals were 0.80 (0.68-0.88), 0.83 (0.73-0.90), 19.06 (7.87-46.19) and 0.88 (0.85-0.91), respectively. There was significant heterogeneity among the included studies but no threshold effect in the meta-analysis. The result of subgroup analysis demonstrated that radiomics based on unenhanced and contrast-enhanced CT possessed higher diagnostic performance, and second-order or higher-order features could enhance the diagnostic sensitivity but also increase the false positive rate. No significant difference in diagnostic ability was observed between studies with machine learning and those without.
UNASSIGNED: The methodological quality and risk of bias of studies investigating the diagnostic performance of radiomics in adrenal tumors should be further improved in the future. CT-based radiomics has the potential benefits in differentiating malignant from benign adrenal tumors. The heterogeneity between the included studies was a major limitation to obtaining more accurate conclusions.
UNASSIGNED: https://www.crd.york.ac.uk/PROSPERO/ CRD 42022331999 .
摘要：
UNASSIGNED：（1）评估研究肾上腺肿块诊断性能的影像组学研究的方法学质量和偏倚风险；（2）通过定量分析确定影像组学在肾上腺肿瘤中的潜在诊断价值。
未经授权：PubMed，Embase,WebofScience,和CochraneLibrary数据库被搜索以获得合格的文献。通过诊断准确性研究2（QUADAS-2）和影像组学质量评分（RQS）评估纳入研究的方法学质量和偏倚风险。通过合并灵敏度评估诊断性能，特异性，诊断优势比(DOR)，和曲线下面积(AUC)。采用Spearman相关系数和亚组分析探讨异质性的原因。使用Deeks漏斗图检查出版偏倚。
UNASSIGNED：确定了28项研究，调查了影像组学在肾上腺肿瘤中的诊断性能，共有3579个样本。平均RQS为5.11（占总数的14.2％），评估者之间的协议可接受（ICC0.94，95％CI0.93-0.95）。根据QUADAS-2的结果，偏倚的风险是中等的。定量分析中包括了九项基于CT的影像组学在区分恶性和良性肾上腺肿瘤中的应用。汇集的敏感性，特异性，95%置信区间的DOR和AUC为0.80（0.68-0.88），0.83（0.73-0.90），19.06（7.87-46.19）和0.88（0.85-0.91），分别。纳入的研究之间存在显著的异质性，但在荟萃分析中没有阈值效应。亚组分析结果表明，基于未增强和对比增强CT的影像组学具有较高的诊断性能，和二阶或更高阶特征可以提高诊断灵敏度，但也会增加假阳性率。在使用机器学习的研究和没有机器学习的研究之间，没有观察到诊断能力的显着差异。
UNASSIGNED：未来应进一步提高研究放射组学在肾上腺肿瘤中的诊断性能的方法学质量和偏倚风险。基于CT的影像组学在区分恶性和良性肾上腺肿瘤方面具有潜在的益处。纳入研究之间的异质性是获得更准确结论的主要限制。
UNASSIGNED：https://www。crd.约克。AC.英国/PROSPERO/CRD42022331999。
未经授权：PubMed，Embase,WebofScience,和CochraneLibrary数据库被搜索以获得合格的文献。通过诊断准确性研究2（QUADAS-2）和影像组学质量评分（RQS）评估纳入研究的方法学质量和偏倚风险。通过合并灵敏度评估诊断性能，特异性，诊断优势比(DOR)，和曲线下面积(AUC)。采用Spearman相关系数和亚组分析探讨异质性的原因。使用Deeks漏斗图检查出版偏倚。
UNASSIGNED：确定了28项研究，调查了影像组学在肾上腺肿瘤中的诊断性能，共有3579个样本。平均RQS为5.11（占总数的14.2％），评估者之间的协议可接受（ICC0.94，95％CI0.93-0.95）。根据QUADAS-2的结果，偏倚的风险是中等的。定量分析中包括了九项基于CT的影像组学在区分恶性和良性肾上腺肿瘤中的应用。汇集的敏感性，特异性，95%置信区间的DOR和AUC为0.80（0.68-0.88），0.83（0.73-0.90），19.06（7.87-46.19）和0.88（0.85-0.91），分别。纳入的研究之间存在显著的异质性，但在荟萃分析中没有阈值效应。亚组分析结果表明，基于未增强和对比增强CT的影像组学具有较高的诊断性能，和二阶或更高阶特征可以提高诊断灵敏度，但也会增加假阳性率。在使用机器学习的研究和没有机器学习的研究之间，没有观察到诊断能力的显着差异。
UNASSIGNED：未来应进一步提高研究放射组学在肾上腺肿瘤中的诊断性能的方法学质量和偏倚风险。基于CT的影像组学在区分恶性和良性肾上腺肿瘤方面具有潜在的益处。纳入研究之间的异质性是获得更准确结论的主要限制。
UNASSIGNED：https://www。crd.约克。AC.英国/PROSPERO/CRD42022331999。
